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Course Skill Level:

Foundational to Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those experienced in Python with basic IT and Linux skills seeking to explore Go and machine learning

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or other attendees with Python skills who want to work through exciting projects to explore the capabilities of Go and Machine Learning.
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them

About this course

Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This course will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured. The course begins by guiding you through setting up your machine learning environment with Go libraries and capabilities. You will then plunge into regression analysis of a real-life house pricing dataset and build a classification model in Go to classify emails as spam or ham. Using Gonum, Gorgonia, and STL, you will explore time series analysis along with decomposition and clean up your personal Twitter timeline by clustering tweets. In addition to this, you will learn how to recognize handwriting using neural networks and convolutional neural networks. Lastly, you’ll learn how to choose the most appropriate machine learning algorithms to use for your projects with the help of a facial detection project. By the end of this course, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound understanding of the practical implementations of machine learning algorithms in real-world projects.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Go Machine Learning expert instructor, students will learn about and explore:
  • ML tasks and Gos machine learning ecosystem
  • Implementing clustering, regression, classification, and neural networks with Go
  • Getting to grips with libraries such as Gorgonia, Gonum, and GoCv for training models in Go
  • Setting up a machine learning environment with Go libraries
  • Using Gonum to perform regression and classification
  • Exploring time series models and decompose trends with Go libraries
  • Cleaning up your Twitter timeline by clustering tweets
  • Learning to use external services for your machine learning needs
  • Recognizing handwriting using neural networks and CNN with Gorgonia
  • Implementing facial recognition using GoCV and OpenCV

Course breakdown / modules

  • What is a problem?
  • What is an algorithm?
  • What is machine learning?
  • Do you need machine learning?
  • The general problem solving process
  • What is a model?
  • On writing and lesson organization
  • Why Go?
  • Quick start
  • Functions
  • Variables

  • The project
  • Exploratory data analysis
  • Linear regression
  • Discussion and further work

  • The project
  • Exploratory data analysis
  • The classifier
  • Naive Bayes
  • Implementing the classifier
  • Putting it all together

  • Exploratory data analysis
  • Decomposition
  • Forecasting

  • The project
  • K-means
  • Data acquisition
  • Exploratory data analysis
  • Data massage
  • Clustering
  • Real data
  • The program
  • Tweaking the program

  • A neural network
  • Linear algebra 101
  • Learning
  • The project
  • Training the neural network
  • Cross-validation

  • Everything you know about neurons is wrong
  • Neural networks a redux
  • The project
  • CNNs
  • Describing a CNN
  • Running the neural network
  • Testing

  • What is a face?
  • PICO
  • GoCV
  • Pigo
  • Face detection program
  • Evaluating algorithms

  • What should the reader focus on?
  • The researcher, the practitioner, and their stakeholder
  • What did this course not cover?
  • Where can I learn more?